Cross-phase skill — question every piece of AI-workflow scaffolding on every model upgrade; remove non-load-bearing pieces one at a time with measurement. Complexity drifts upward by default; subtraction requires discipline. Governs scaffolding built around AI usage (prompts, wrapper scripts, guardrails, validation steps), not the task's application code — for code-level minimalism defer to the ralphctl-ponytail, ralphctl-surgical-simplicity, and ralphctl-karpathy-guidelines skills when installed in this session.
Model software around the business domain using bounded contexts, aggregates, and ubiquitous language. Use when the user mentions "domain modeling", "bounded context", "aggregate root", "ubiquitous language", "anti-corruption layer", "context mapping", "domain events", "strategic design", "the code doesnt match the business", or "how do we split this big system". Also trigger when breaking a monolith into services, defining service boundaries, or aligning code structure with business processes. Covers entities vs value objects, domain events, and context mapping strategies. For architecture layers, see clean-architecture. For complexity, see software-design-philosophy.
Refines raw ideas into sharp, actionable concepts through structured divergent and convergent thinking. Use when an idea is still vague, when you need to stress-test assumptions before committing to a plan, or when you want to expand options before converging on one. Triggers on "ideate", "refine this idea", or "stress-test my plan".
Cross-phase skill — establish a shared understanding of what will and will not be done before producing output: restate the input back to the user, surface assumptions, name non-goals, and agree before you write. For an input that is still a raw, unshaped idea needing multiple candidate directions before one is chosen, run the ralphctl-idea-refinement skill first when it is installed in this session; alignment then confirms whichever direction comes out of it.
Session-discipline skill — settle the plan before touching code, work in small verified increments with a feedback loop that proves each change works, and record corrections so the same mistake is not repeated. Use when executing multi-step coding tasks where batching unverified work would compound risk; bundles plan-first sequencing and learning capture on top of the narrower per-change habits in ralphctl-iterative-review and (when installed) ralphctl-karpathy-guidelines.
Behavioural guardrails against four empirical LLM coding failure modes — silent assumptions, over-complication, orthogonal damage to code you don't fully understand, and declaring done without verification. Use when writing, reviewing, or refactoring code to surface assumptions early, keep changes proportionate to the task, and loop until explicit success criteria pass; broader than the implementation-choice ladder in ralphctl-ponytail or the scope checklist in ralphctl-surgical-simplicity (when those are installed), which it complements rather than duplicates.
Anti-over-engineering ladder for choosing HOW to implement something — before writing custom code, climb the rungs in order and stop at the first that holds. Does this need to exist at all (YAGNI) → already in the codebase → standard library → native platform feature → already-installed dependency → one line → minimum code that works. Use when picking an implementation approach or evaluating a new dependency; for keeping the footprint of an already-chosen approach small, defer to the ralphctl-surgical-simplicity skill when installed.
Execute-phase scope-containment checklist — write the minimum code the task needs and touch only the files and lines the task requires; surface out-of-scope findings as notes rather than fixing them inline. Governs the footprint of a change whose approach is already decided; for choosing that implementation approach itself, defer to the ralphctl-ponytail skill when installed.